✨Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning

📝 Summary:
This paper addresses Preference Mode Collapse PMC in text-to-image diffusion models, where models lose diversity despite high reward scores. It introduces D^2-Align, a framework that mitigates PMC by directionally correcting the reward signal during optimization. This novel approach maintains gen...

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24146
• PDF: https://arxiv.org/pdf/2512.24146

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#DiffusionModels #ReinforcementLearning #GenerativeAI #MachineLearning #AIResearch

✨DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer

📝 Summary:
DreamID-V is a novel video face swapping framework that uses diffusion transformers and curriculum learning. It achieves superior identity preservation and visual realism by bridging the image-to-video gap, outperforming existing methods and enhancing temporal consistency.

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01425
• PDF: https://arxiv.org/pdf/2601.01425
• Project Page: https://guoxu1233.github.io/DreamID-V/
• Github: https://guoxu1233.github.io/DreamID-V/

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#FaceSwapping #DiffusionModels #ComputerVision #GenerativeAI #VideoAI

✨BitNet Distillation

📝 Summary:
BitNet Distillation fine-tunes LLMs to 1.58-bit precision using SubLN, attention distillation, and continual pre-training. It achieves comparable performance to full-precision models, offering 10x memory savings and 2.65x faster inference.

🔹 Publication Date: Published on Oct 15, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.13998
• PDF: https://arxiv.org/pdf/2510.13998
• Github: https://github.com/microsoft/BitNet

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#LLM #Quantization #ModelCompression #DeepLearning #AI

✨NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation

📝 Summary:
NextFlow is a unified decoder-only transformer enabling fast multimodal understanding and generation. It uses next-token prediction for text and next-scale for images, generating 1024x1024 images in 5 seconds. It achieves state-of-the-art performance among unified models.

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02204
• PDF: https://arxiv.org/pdf/2601.02204
• Github: https://github.com/ByteVisionLab/NextFlow

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✨Can LLMs Predict Their Own Failures? Self-Awareness via Internal Circuits

📝 Summary:
Large language models (LLMs) generate fluent and complex outputs but often fail to recognize their own mistakes and hallucinations. Existing approaches typically rely on external judges, multi-sample ...

🔹 Publication Date: Published on Dec 23, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20578
• PDF: https://arxiv.org/pdf/2512.20578
• Github: https://github.com/Amirhosein-gh98/Gnosis

🔹 Models citing this paper:
• https://huggingface.co/AmirhoseinGH/Gnosis-Qwen3-1.7B-Hybrid
• https://huggingface.co/AmirhoseinGH/Gnosis-Qwen3-4B-Instruct-2507
• https://huggingface.co/AmirhoseinGH/Gnosis-Qwen3-4B-Thinking-2507

==================================

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨VAR RL Done Right: Tackling Asynchronous Policy Conflicts in Visual Autoregressive Generation

📝 Summary:
Visual autoregressive models face training instability due to asynchronous policy conflicts, which are addressed through a novel framework enhancing group relative policy optimization with intermediat...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02256
• PDF: https://arxiv.org/pdf/2601.02256

==================================

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✨Talk2Move: Reinforcement Learning for Text-Instructed Object-Level Geometric Transformation in Scenes

📝 Summary:
Talk2Move presents a reinforcement learning-based diffusion framework that enables precise, semantically faithful spatial transformations of objects in scenes using natural language instructions. AI-g...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02356
• PDF: https://arxiv.org/pdf/2601.02356
• Project Page: https://sparkstj.github.io/talk2move/
• Github: https://github.com/sparkstj/Talk2Move

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✨KV-Embedding: Training-free Text Embedding via Internal KV Re-routing in Decoder-only LLMs

📝 Summary:
KV-Embedding enables training-free representation learning from frozen LLMs by utilizing key-value states for enhanced context access and automated layer selection. AI-generated summary While LLMs are...

🔹 Publication Date: Published on Jan 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01046
• PDF: https://arxiv.org/pdf/2601.01046

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✨VINO: A Unified Visual Generator with Interleaved OmniModal Context

📝 Summary:
VINO is a unified visual generator that uses a shared diffusion backbone with multimodal inputs to perform image and video generation and editing tasks. AI-generated summary We present VINO, a unified...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02358
• PDF: https://arxiv.org/pdf/2601.02358
• Project Page: https://sotamak1r.github.io/VINO-web/
• Github: https://github.com/SOTAMak1r/VINO-code

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✨K-EXAONE Technical Report

📝 Summary:
K-EXAONE is a multilingual language model with a Mixture-of-Experts architecture that achieves competitive performance on various benchmarks while supporting multiple languages and long-context window...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01739
• PDF: https://arxiv.org/pdf/2601.01739
• Github: https://github.com/LG-AI-EXAONE/K-EXAONE

🔹 Models citing this paper:
• https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B

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✨Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling

📝 Summary:
Falcon-H1R is a 7B-parameter language model that achieves competitive reasoning performance through efficient training strategies and architectural design, enabling scalable reasoning capabilities in ...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02346
• PDF: https://arxiv.org/pdf/2601.02346

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✨OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment

📝 Summary:
OpenNovelty is an LLM-powered agentic system for verifiable scholarly novelty assessment in peer review. It retrieves and analyzes prior work via semantic search and taxonomy construction, generating evidence-backed reports grounded in real papers. This tool aims to promote fair, consistent, and ...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01576
• PDF: https://arxiv.org/pdf/2601.01576
• Project Page: https://www.opennovelty.org/
• Github: https://github.com/january-blue/OpenNovelty

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✨COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs

📝 Summary:
COMPASS evaluates large language models' compliance with organizational policies, revealing significant gaps in enforcing prohibitions despite strong performance on legitimate requests. AI-generated s...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01836
• PDF: https://arxiv.org/pdf/2601.01836
• Github: https://github.com/AIM-Intelligence/COMPASS

🔹 Models citing this paper:
• https://huggingface.co/AIM-Intelligence/COMPASS_Qwen2.5-7B-Instruct_LoRA
• https://huggingface.co/AIM-Intelligence/COMPASS_gemma-3-4b-it_LoRA

✨ Datasets citing this paper:
• https://huggingface.co/datasets/AIM-Intelligence/COMPASS-Policy-Alignment-Testbed-Dataset
• https://huggingface.co/datasets/AIM-Intelligence/COMPASS-Policy-aware-SFT-Dataset

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✨Project Ariadne: A Structural Causal Framework for Auditing Faithfulness in LLM Agents

📝 Summary:
Project Ariadne uses structural causal models and counterfactual logic to evaluate the causal integrity of LLM reasoning, revealing a faithfulness gap where reasoning traces are not reliable drivers o...

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02314
• PDF: https://arxiv.org/pdf/2601.02314
• Github: https://github.com/skhanzad/AridadneXAI

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✨GARDO: Reinforcing Diffusion Models without Reward Hacking

📝 Summary:
Online reinforcement learning for diffusion model fine-tuning suffers from reward hacking due to proxy reward mismatches, which GARDO addresses through selective regularization, adaptive reference upd...

🔹 Publication Date: Published on Dec 30, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24138
• PDF: https://arxiv.org/pdf/2512.24138
• Project Page: https://tinnerhrhe.github.io/gardo_project/
• Github: https://github.com/tinnerhrhe/gardo

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✨IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset

📝 Summary:
A large-scale public multi-annotator skin lesion segmentation dataset is introduced with extensive metadata for annotator analysis and consensus modeling. AI-generated summary Multi-annotator medical ...

🔹 Publication Date: Published on Dec 25, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21472
• PDF: https://arxiv.org/pdf/2512.21472
• Github: https://github.com/sfu-mial/IMAplusplus

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✨Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion

📝 Summary:
A semi-supervised remote sensing image segmentation framework combines vision-language and self-supervised models to reduce pseudo-label drift through dual-student architecture and semantic co-guidanc...

🔹 Publication Date: Published on Dec 28, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23035
• PDF: https://arxiv.org/pdf/2512.23035
• Project Page: https://xavierjiezou.github.io/Co2S/
• Github: https://github.com/XavierJiezou/Co2S

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✨Recursive Language Models

📝 Summary:
Recursive Language Models RLMs allow LLMs to process arbitrarily long prompts. RLMs programmatically decompose prompts and recursively call the LLM over snippets. This extends input length 100x and improves performance, even for shorter prompts, at similar cost.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24601
• PDF: https://arxiv.org/pdf/2512.24601
• Github: https://github.com/alexzhang13/rlm/tree/main

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#LLMs #AI #NLP #RecursiveLMs #LongContext

✨InfiniteVGGT: Visual Geometry Grounded Transformer for Endless Streams

📝 Summary:
InfiniteVGGT enables continuous 3D visual geometry understanding for infinite streams. It uses a causal transformer with adaptive rolling memory for long-term stability, outperforming existing streaming methods. A new Long3D benchmark is introduced for rigorous evaluation of such systems.

🔹 Publication Date: Published on Jan 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02281
• PDF: https://arxiv.org/pdf/2601.02281
• Github: https://github.com/AutoLab-SAI-SJTU/InfiniteVGGT

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#VisualGeometry #3DVision #Transformers #StreamingAI #DeepLearning